Figure 1: GDP relative to 2009Q2 trough (blue), relative to 2001Q4 trough (red), in Ch2009$, in logs. A reading of 0.12 can be interpreted as meaning output was 12% higher than it was at NBER defined trough. Source: BEA, 2015Q2 advance release, NBER, and author’s calculations.

The gap falls to 2.9% when per capita GDP is compared.

One interesting aspect of the slow recovery is the fact that government spending has been particularly low, as others have pointed out [1] (and contra common perceptions). Perhaps, more interesting is the source of the government spending that pushed output growth in the 2000’s: defense.

Figure 2: Contributions to differences in 2009Q2 and 2001Q4 recoveries, in billions of Ch.2009$, SAAR, from defense spending (blue) and all other spending (red). Source: BEA, 2015Q2 advance release, and author’s calculations.

In other words, most of the slower growth in an accounting sense can be attributed to lower defense spending. Does this mean we should embark upon another war? After all, the same voices who argued for invading Iraq have also argued for taking military action against Iran (e.g.., John Bolton). I do believe doing so would add to aggregate demand. Figure 3 (from this post) shows how much we spent up to FY2012 in real terms.

Figure 3 incorporates only direct fiscal costs to the United States government, and excludes interest costs. See here for another tabulation.

However, believe it or not, there are other ways of boosting aggregate demand, even when restricting oneself to spending on goods and services – and that’s spending on directly productive assets, such as infrastructure. To make this point concrete, note that in dollar terms, overall government spending more than accounts for the GDP differential. GDP was 371.7 Ch.09$ billion (SAAR) lower than the corresponding point in the previous recovery. Government spending on goods and services was 559.4 Ch.09$ billion less. As noted in this post, a big program of spending on infrastructure would clearly benefit the economy both on the supply and demand side. And yet, there is no evidence of movement here, particularly given the refusal of certain elements to consider more tax funding for such measures.[2]

]]>http://econbrowser.com/archives/2015/08/recovery-without-military-keynesianism/feed16Guest Contribution: “Should Emerging Markets Fear A Fed Lift-Off?”http://econbrowser.com/archives/2015/07/guest-contribution-should-emerging-markets-fear-a-fed-lift-off
http://econbrowser.com/archives/2015/07/guest-contribution-should-emerging-markets-fear-a-fed-lift-off#commentsFri, 31 Jul 2015 18:03:16 +0000http://econbrowser.com/?p=24512Today we are fortunate to have a guest contribution written by Carolina Osorio Buitron, Esteban Vesperoni, and Prakash Loungani, from the Research Department of the International Monetary Fund. The views expressed in this blog are solely those of the authors and do not necessarily represent the views of the IMF, its management, nor its Executive Board.

Though this week’s FOMC statement is still being parsed, market participants generally expect the Federal Reserve to raise policy interest rates this September. In contrast, the European Central Bank has significantly eased monetary policies over the past year and is expected to maintain accommodative policies for a substantial period of time. Should emerging markets fear the consequences of the so-called Fed liftoff and the likely increase in U.S. long-term bond yields?

Analysis in the IMF’s latest Spillover Report suggests the answer is “no”. Good news about economic growth in the U.S. raises economic activity in emerging markets and in other advanced economies (henceforth referred to for convenience just as ‘emerging markets’), despite the associated rise in bond yields.

Rates and reasons

Bond yields in major advanced economies such as the United States could rise for many reasons. The analysis in the IMF’s report–and in a comprehensive background note–tries to uncover the underlying shocks behind the rise and show that the impact on other countries depends on why bond yields rise in the first place.
Specifically, three types of shocks are identified based on the joint behavior of bond yields and stock prices (a strategy that originated with Matheson and Stavrev):

‘Risk’ shocks: Movements in bond yields and stock prices associated with movements in the Volatility Index (VIX) are assumed to capture changes in risk appetite.

Monthly data on 10-year bond yields and stock prices from 1994 to the present is used to identify the shocks; real and money shocks for the U.S. and euro area are identified in a unified two-economy framework.
The relative contribution of the shocks to U.S. and euro area bond yields is illustrated in Figure 1 for the period since mid-2013. The increase in U.S. bond yields over 2014 was in large part driven by positive real shocks—that is, good news about growth prospects (panel 1)—whereas the decline in euro area yields reflected both real and money shocks—that is, weaker growth prospects and perceptions of easier monetary policy (panel 2).

The reason matters

The big payoff comes when we estimate the impact of the real and money shocks on emerging economies. The findings are shown in Figure 2. The bars show the impact of a 1 percentage point increase in bond yields in either the U.S. or the euro area on bond yields, net capital inflows and industrial production in other economies. The impact shown is the average across the various economies.

The striking feature of the results is that real and money shocks in either the U.S. or the euro area have vastly different spillover effects on other economies. This can be seen by comparing the panels on the left with those on the right. While both types of shocks lead to an increase in bond yields in other economies, real shocks lead to higher capital inflows and an increase in industrial production, whereas money shocks do the opposite.

It may appear surprising that higher growth in the United States or euro area leads to higher flows to emerging markets. There are two channels at play here. First, there is the ‘traditional channel’ through which a growth shock in the U.S. (or euro area) induces capital to flow to the country where the shock originates and causes an appreciation of the dollar (or the euro). Second, there is a ‘risk appetite channel’, through which a real shock boosts investor risk-appetite—investment in emerging markets increases as better economic prospects are envisaged at the global level. This channel would cause capital to flow to emerging markets and their currencies to appreciate. Our results suggest that the second effect dominates—likely related to size of capital flows out of emerging and systemic countries.

The results also show that both U.S. and euro area real shocks have positive spillovers on other economies. Hence, spillovers to emerging economies could be amplified when there is good news about growth prospects in both the United States and the euro area (“synchronous” episodes of positive real shocks) and dampened when there is good news about one but not the other.

There are also some differences in spillovers across regions, reflecting different economic links with the United States and Europe. For instance, the effect of a real shock in the euro area is considerably larger in Emerging Europe than in other regions owing to stronger trade links. Real shocks in both the United States and Europe generate larger capital inflows to Asia than to other regions, partly because the sample of Asian economies includes two world financial hubs—Hong Kong SAR and Singapore—that experience much larger capital inflows than other emerging markets.

Good for them, good for others

To sum up, monetary policies in major advanced economies like the United States and the euro area can generate positive spillovers for other countries. These policies are the appropriate domestic response to the situation these countries face of output below potential and inflation below target. Closing output gaps will, in turn, will lower unemployment (as one of us noted in a previous Econbrowser post) and raise investment (as the IMF’s First Deputy Managing Director David Lipton noted recently).

The analysis in the spillover report suggests that these actions will also have a positive impact on economic activity in other countries if they are perceived as good news about growth prospects in advanced economies. Of course, if advanced economy interest rates rise for reasons other than improved growth prospects, this could be associated with lower economic activity in other economies. However, since monetary policy actions are expected to close output gaps (i.e. improve growth prospects), the likely scenario is the one shown in the left panels of Figure 2.

Though not discussed in the latest report, the advice given in the previous IMF spillover reports on additional steps that major advanced economies can take to ensure positive spillovers remains valid:

First, central banks in advanced economies should communicate their policy intentions clearly and to maintain a dialogue with other central banks. “Do what you need to do, but work with others too” should be the mantra.

Second, financial sector measures (for example, cleaning up of non-performing loans) can ensure that the monetary transmission mechanism is working, so that monetary accommodation leads to output gaps being closed instead of feeding a search for yield.

Third, the policy mix used to achieve domestic goals can be adjusted to some extent. Growth-friendly fiscal policies in advanced economies can also help close output gaps—with different exchange rate impacts than monetary policy—and also raise potential output. In this context, one area where more could be done is government spending on infrastructure. Increased public infrastructure investment raises output in both the short and long term, particularly during periods of economic slack and when investment efficiency is high. In many countries, where borrowing costs are low and demand is weak, debt-financed projects could have large output effects, without increasing the debt-to-GDP ratio. The IMF’s analysis shows that infrastructure investment can have a quick and positive effect on both employment and output, which is useful in an environment of weak demand and high unemployment.

This post written by Carolina Osorio Buitron, Esteban Vesperoni, and Prakash Loungani.

]]>http://econbrowser.com/archives/2015/07/guest-contribution-should-emerging-markets-fear-a-fed-lift-off/feed0Current economic conditionshttp://econbrowser.com/archives/2015/07/current-economic-conditions-2
http://econbrowser.com/archives/2015/07/current-economic-conditions-2#commentsThu, 30 Jul 2015 20:23:09 +0000http://econbrowser.com/?p=24504The Bureau of Economic Analysis announced today that U.S. real GDP grew at a 2.3% annual rate in the second quarter. You can’t describe the new data as favorable, but I’m still hopeful about what comes next.

U.S. real GDP growth at an annual rate, 1947:Q2-2015:Q2, historical average rate (3.1%), and average since 2009:Q3 (2.1%).

GDP growth since the end of the Great Recession in 2009:Q2 has averaged 2.1% per year, a full percentage point below the average over the entire 1947-2015 period. And that 3.1% includes both recessions and expansions. Moreover, the benchmark revision of the last three years of data that accompanied today’s report didn’t help. Although the new data revise the weak numbers for the first quarters of 2015 and 2014 up a bit, the BEA now estimates that annual GDP growth averaged 1.9% (logarithmically) over 2012:Q1-2015:Q1, down 0.3% from the 2.2% that had initially been reported for that period. Jason Furman attributes much of the downward revision to “a new methodology for calculating the price of financial services spending and revisions to source data on services.” In any case, the bottom line is that the post-2009 expansion, which we already knew was very weak by historical standards, now appears to have been even weaker.

Real GDP growth at an annual rate as reported on April 29 and July 30.

The recent weakness has brought the Econbrowser Recession Indicator Index up to 13.3%. The index has now shown a modest spike up with each of the last three weak winters. The index uses today’s data release to form a picture of where the economy stood as of the end of 2015:Q1.

GDP-based recession indicator index. The plotted value for each date is based solely on information as it would have been publicly available and reported as of one quarter after the indicated date, with 2015:Q1 the last date shown on the graph. Shaded regions represent the NBER’s dates for recessions, which dates were not used in any way in constructing the index, and which were sometimes not reported until two years after the date.

In terms of the drivers of the 2.3% Q2 growth, there’s really only one story– consumer spending. While exports made a technical contribution to the change from Q1, this was only a partial rebound from the exceptionally low Q1 exports, which had been temporarily reduced as a result of Q1 work disruptions at west coast ports.

It’s interesting to speculate on the role of oil prices in the recent GDP numbers. The fall in oil prices since last summer was of course a boon for oil consumers and a bane for oil producers. Usually consumers respond pretty quickly to windfalls in spending power, whereas oil producers take significantly longer to cut back their investments. But this time the consumer response was more subdued, while the lead times for adjusting modern fracking drilling are much shorter than for conventional oil. Lower investment in the oil patch may be having an effect on the GDP aggregates.

So where are the grounds for optimism in any of this? I continue to believe that the next two graphs are very important to keep track of, which show expenditures on motor vehicles and parts and residential construction as percentages of GDP. These are two of the most important cyclical variables, accounting for a disproportionate share of the growth during booms and disproportionate share of the loss during busts. Both remain quite low today relative to their historical averages. When they return closer to historical levels (and I believe they will), that will give some boost to GDP growth. We haven’t seen it so far because the overhang from the burst of the housing bubble and associated debt problems was so big, it’s taken this long to work out from under it.

And once housing rebounds, the added income and sales will help bring other investment up with it. As Bill McBride has long been emphasizing:

In the graph, red is residential [investment], green is equipment and software, and blue is investment in non-residential structures. So the usual pattern– both into and out of recessions is– red, green, blue.

None of this is to claim that the U.S. can expect to average 3.1% growth again on a long-term basis. With slower growth of the working-age population, that won’t happen. But we nevertheless should expect a positive cyclical contribution when housing regains its traction.

Some individuals have touted trends in Wisconsin’s median household income relative to Minnesota and the Nation. The Census Bureau warns researchers from doing intertemporal comparisons using the standard series (e.g., on FRED), given data breaks due to different methods of interpolation. Here I plot the American Community Survey (ACS) series for Minnesota, Wisconsin and US, which are not subject to the same problem of comparability, and are estimated with greater precision due to larger sample sizes.

Figure 1: Median household income for Minnesota (blue), Wisconsin (red), and US (black). A reading of 0.045 means that the value is 4.5% higher than it was in 2010. Source: American Community Survey/Census Bureau, and author’s calculations.

Use of this series reverses the impression from the standard series that the gap between Wisconsin and Minnesota closed going from 2012 to 2013 (see Figure 6 in this post). In fact, the gap has been widening since 2010.

Interestingly, the US spread is larger than it was in April when I last examined the spreads, which seems to suggest that a US slowdown is not imminent. Of course, there is no guarantee that in an era of zero short rates, quantitative easing and (over an even longer period) foreign central bank purchases of long term Treasurys, the traditional correlation between spreads and growth (and recessions) hold. The only other country with a negative spread is Brazil.

Since the levels of the short rate are also important for predicting recessions, as reported in just-published Chinn and Kucko (2015) (Table 6), I also present the underlying short and long rates in Figure 2.

Figure 2: Three month term interest rates (blue bar) and ten year interest rates (red bar), as of 7/27/2015 (blue bar). China long term observation is Five year interest rate. Euro ten year rate is for Germany. Source: Economist accessed 7/27/2015, and author’s calculations.

Hence, while spreads are currently positive in most countries, short rates are also high, and particularly so in Australia, India and Brazil.

Now, shrinking spreads usually signal slower growth six to 12 months ahead, at least in the US context, so it could be that the spreads were signalling earlier the imminent recession. I don’t off-hand have those spreads from six to 12 months ago, but I did record the spreads in the post back in April, about three and a half months ago. Figure 3 repeats the relevant graph.

Back then, China, Australia, India and Brazil all recorded inversions, although as I mentioned at the time, there was little statistical evidence I knew for the predictive power of term spreads in these countries. As it turns out, slowdowns — although not necessarily recessions — did manifest in China and Brazil.

Interestingly, the yield curve did not invert in Canada, either in April (as shown in Figure 3) or before; nonetheless the country has just recorded one quarter of negative growth (-0.6% SAAR in Q1). The closest to inversion was +0.75% in January 2015. But then, in Chinn and Kucko (Table 3), we didn’t find evidence that the Canadian spread predicted growth in the 1998-2013 period.

Real Indicators for the US

At this juncture, I think it is useful to examine the data. Figure 4 depicts five key indicators examined by the NBER Business Cycle Dating Committee for identifying peaks and troughs: nonfarm payroll employment, industrial production, personal income ex.-transfers (Ch.09$), manufacturing and trade sales (Ch.09$), and monthly GDP from Macroeconomic Advisers (Ch.09$). I normalize on the end of 2014 as that is the peak in industrial production.

While industrial production is 0.7% (log terms) below peak, the other series are as of latest recorded data still rising. Since the later observations will be revised (which is why the NBER BCDC waits months to make decisions), one would not want to “pull a Lazear” or (heavens forbid) “pull a Luskin”, and assert “no recession imminent”. However, at the moment, growth — albeit slow — seems the likely outcome for the US.

However, for my taste, the slowdown in industrial production (and manufacturing) is worrying, and given the persistent strength in the dollar, one would not want to be too complacent. To the extent that policies can be implemented to keep the dollar from appreciating further, they should be.

The World?

World trade volumes have taken a bit of a dive, and some have taken this to mean a global slowdown. Does this signify a recession? The answer to this question hinges upon the extent the trade slowdown in the wake of the global financial recession is structural (e.g., less deepening of vertical specialization) or cyclical. There’s some debate in Chapters 1-3 in this VoxEU volume.

The July World Economic Outlook update doesn’t predict a global recession, but it certainly highlights downturns in certain areas (2015 q4/q4 growth in Latin America/Caribbean is forecasted at -0.1%, while Russia is slated for -4.8%. China is forecasted for 6.8%, down from 7.3% in 2014.

That being said, it’s an open question how resilient East Asian growth will be to a more than anticipated deceleration in growth. Then, prospects for a global recession depend a lot on how a global recession is defined — a marked slowdown or an actual decrease in global output.

“Wisconsin’s doing terribly. It’s in turmoil. The roads are a disaster because they don’t have any money to rebuild them. They’re borrowing money like crazy. They projected a $1 billion surplus, and it turns out to be a deficit of $2.2 billion. The schools are a disaster. The hospitals and education was a disaster. And he was totally in favor of Common Core, which I hate!”

I can’t say I agree with much of what Mr. Trump says, but even a broken clock is right twice a day. (I don’t hate common core, but he is right that Governor Walker was for common core before he was against it…) And the WaPo article goes through some material documenting the correctness of several of the assertions.

As Jon Peacock at Wisconsin Budget Blog notes, the apparently small structural budget deficit of $210 million hides an enormous shortfall implicit in assumptions that agencies will underspend (“lapses”) billions of dollars over the next three fiscal years, and return those funds to the state treasury.

[T]he assumption that state agencies will lapse about $2.15 billion over a 3-year period is a convenient way to make the structural deficit look relatively manageable, compared to many of the past structural deficits. However, it also means that the structural deficit figure by itself is no longer a very good indicator of the state’s future fiscal challenges, because that figure leaves out the immense challenge of lapsing so much funding year after year.

Given the fact that typical lapses are between $250-$300 million/year [1], assumptions of something like $700 million year imply that 0.15% of gross state product worth of additional cuts and tax increases will be necessary each of the next three fiscal years, by my back-of-the-envelope reckoning.

And of course, civilian and private employment continue to decline [1]. As a reminder, Wisconsin employment is now below pre-recession peak levels. In other words, Wisconsin is reverting.

]]>http://econbrowser.com/archives/2015/07/who-am-i-to-disagree/feed4Measuring unemploymenthttp://econbrowser.com/archives/2015/07/measuring-unemployment
http://econbrowser.com/archives/2015/07/measuring-unemployment#commentsSat, 25 Jul 2015 14:46:22 +0000http://econbrowser.com/?p=24437New claims for unemployment insurance this week came in at the lowest level in over 40 years. How much slack can there be left in the labor market?

The most common measure of unemployment (known as U3) counts the number of people who are not currently working and are actively looking for a job. You’re put in that category by the BLS if you report taking active measures over the last month to find work. In June U3 amounted to 5.3% of the labor force, where the labor force is defined as the sum of U3 plus people who are currently employed. That’s well below the average rate of 6.5% seen over the last 40 years.

But if you simply count the number of people who are working as a percent of the population 16 years and over, you come up with only 59%. That’s up a little from the lows reached during the Great Recession, but significantly below what it had been over the last several decades.

The difference between the last two graphs is explained by people who are not working but also are not counted as unemployed by the BLS. Most of these people don’t want a job because they are retired, disabled, in school, or other reasons. But there are a number of people who aren’t working, say they want a job, are available for work, and have taken measures within the past year to try to find work. But because they did not do anything active within the last month, they aren’t counted as “unemployed” or “in the labor force”. Instead they are designated by the BLS as “marginally attached to the labor force”. When these individuals are added to those counted as unemployed by the conventional designation, we get a measure of unemployment known as U5. The “marginally attached” are sometimes further broken down into those who say they didn’t search within the last month because they were discouraged about finding a job, and those who give some other reason.

And there are a number of people who say they’re employed, but only part-time, and are hoping to get a full-time position. When we add these to the U5 count, we get the broader unemployment measure known as U6. Last month the U.S. unemployment rate as measured by U3 was 5.3%, but when measured by U6, it came in at 10.5%.

Which, if any, of these is the appropriate measure of labor market slack? Some recent research by Andreas Hornstein and Marianna Kudlyak from the Federal Reserve Bank of Richmond and Fabian Lange from McGill University suggested a useful way to try to answer this question. If we’re not sure how to interpret the answers people give to questions posed by the BLS, why not look at what those answers imply for what actually ends up happening? Of those people in U3 who have been unemployed for 26 weeks or less, on average over the 1994-2013 period 28% of them would find a job the next month. But of those included in U3 who have been unemployed for more than 26 weeks, on average only 14% of them would find a job the next month. For comparison, if we look at those designated as marginally attached to the labor force, 13% of them would typically find a job the next month. And of those who say they want a job but don’t report having actively searched within the last year, 14.5% would typically be employed the following month. These numbers suggest we should treat the “marginally attached”, and for that matter everybody who says they want a job even if they haven’t been actively looking for one, the same way we treat the long-term unemployed in U3.

Of those who say they are not working, not looking for work, and not retired or disabled, about 8% on average would likely be working the next month.

These considerations led Hornstein, Kudlyak, and Lange (2014) to propose a Non-Employment Index that is a weighted average of all those who are not currently working with weights based on the average probabilities in the table above. A weight of one is given to those in U3 who have been unemployed for 26 weeks or less, a weight of about one-half to others in U3 and everyone else who is not employed but say they want a job, a weight of about one-quarter to non-employed students, and so on. Since one of the things we are doing with this index is changing the concept of what we mean by the “labor force,” the authors report their measure as a percent of the noninstitutional civilian population 16 years and over. For comparison I’ve plotted their NEI series in the graph below along with U3 and U6 unemployment as percent of the population. All the numbers in the graph are based on seasonally unadjusted data, from which I then calculated 12-month averages to get a quick seasonally adjusted series. I’ve added those employed part-time for economic reasons to the NEI for more direct comparability with U6. The graph shows that U6 unemployment rose faster during the Great Recession and came down more dramatically since than does either the narrower measure based on U3 or the broader measure based on NEI.

U3 unemployment, U6 unemployment, and NEI plus part-time employment for economic reasons as percent of noninstitutional population 16 years and over, averages of seasonally unadjusted values over preceding 12 months, 1995:M1 to 2015:M6.

What does this tell us about how tight the labor market is at the moment? One way we could try to answer that is to look at the historical relation between the conventional measure U3 and the new proposed NEI. I performed an analysis similar to those that the authors report in their paper, regressing seasonally adjusted U3 on a constant and seasonally adjusted NEI over the period 1994:M1 to 2007:M6. The fitted values of this regression are plotted in blue in the graph below, which closely track the actual values of U3 in black. I then asked, what values would we have predicted for U3 over the more recent period based on the historical relation? The answer is plotted as the dashed blue line. U3 unemployment was much higher over 2009-2013 than we would have expected based on the calculated value of NEI, but the two indicators have been back in line over the last year and a half.

Seasonally adjusted values of U3 and predicted values from regression of U3 on NEI.

This is the opposite from the conclusion that many might have anticipated. The level of NEI is always higher than U3 at every date, but this does not mean that there is always more slack in the labor market than analysis based on U3 would predict. The question is whether the difference between NEI and U3 is bigger or smaller than usual. During the Great Recession, the difference was smaller than usual. The authors conclude:

Contrary to the extended BLS unemployment rates, we find that for the post-2007 period U3 actually overstates unemployment relative to the NEIs that exclude those working [part time for economic reason]. This break relative to the pre-2007 relation is due to the exceptionally large increase of long-term unemployment following the Great Recession. Since our NEIs down-weight long-term unemployed significantly relative to short-term unemployed, the NEIs increase less than U3 after the Great Recession.

It is interesting to relate this to another observation: inflation during and following the Great Recession came in higher than one would have predicted using a Phillips Curve based on the traditional measure U3– with U3 so high, it’s surprising that inflation did not drop more dramatically. A while back I discussed one possible interpretation of this based on the hypothesis that price-setters were slow in adapting inflation expectations to the changing environment. An alternative hypothesis is that the surge in long-term unemployment made U3 a less useful predictor of inflation. Evidence that short-term unemployment is a better predictor of inflation than U3 was provided by Stock (2011) and Watson (2014), though Kiley (2014) reached a different conclusion using state-level data.

If the Hornstein, Kudlyak and Lange measure is correct, then there is substantially less uncertainty about how to measure unemployment than many of us have been assuming. The first impression from traditional labor market indicators such as U3 and initial unemployment claims– namely, that we’re returning to a period of tightness in the labor market– is the correct one. And the period when forecasting equations based on U3 under predict inflation may have come to an end.

]]>http://econbrowser.com/archives/2015/07/measuring-unemployment/feed22Guest Contribution: “Time to Re-estimate the U.S. Output Gap: Have the Scars from the Crisis Healed?”http://econbrowser.com/archives/2015/07/guest-contribution-time-to-re-estimate-the-u-s-output-gap-have-the-scars-from-the-crisis-healed
http://econbrowser.com/archives/2015/07/guest-contribution-time-to-re-estimate-the-u-s-output-gap-have-the-scars-from-the-crisis-healed#commentsFri, 24 Jul 2015 05:10:10 +0000http://econbrowser.com/?p=24402Today we are fortunate to present a guest contribution written by Ali Alichi, senior economist at the International Monetary Fund. The views expressed in this blog are solely those of the author and do not necessarily represent the views of the IMF, its management, nor its Executive Board.

Models with deterministic trends, such as the HP filter or the “hybrid” approach of the Congressional Budget Office (CBO) produce unreliable estimates for the potential output. For example, excess capacity built in the run-up to the Global Financial Crisis (GFC) should be treated as cyclical, but deterministic-trend models initially treat it as permanent, leading to an overestimation of potential output and a large output gap in the aftermath of the Crisis. In a recent paper, we have developed a multivariate (MV) filtering methodology, which tackles this issue for the U.S. economy, among other desirable features.

Methodology

The MV filtering methodology extracts the unobservable potential output from observables (output, employment indicators, capacity utilization, inflation indicators, etc). This is done by (i) breaking all variables into structural and cyclical (shock) components, (ii) writing down the economic relationship between variables, e,g,, the Phillips curve, which connects output to inflation, and (iii) estimating the whole system using the data for observables and economic relationships for unobservables. In particular, estimates draw on labor market data, capacity utilization, inflation, and inflation expectations. Not all of this data and economic relationships usually enter traditional estimates, making it hard for them to identify which shocks have longer lasting effects on potential.

Findings

Potential growth has gradually recovered toward 2 percent after the GFC. Figure 1 shows the paths of (real) GDP and potential GDP growth.1 In the second half of 1990s, potential growth reached about 3½ percent, but since early 2000s, it started falling. The GFC brought activity to a halt and potential output growth to the negative territory. Potential growth started to pick up in 2010, and is currently estimated at around its steady state growth rate of about 2.0 percent. Potential growth is expected to surpass its steady state value in the next 2-3 years, as the recovery picks up steam but will slow down to its steady state value of 2.0 percent as demographic effects set in.

The U.S. output gap has shrunk considerably since the GFC, but is still negative. The U.S. output gap reached about -5¼ percent, following the GFC, but has since been recovering. By end-2014, the output gap stood at around -2.0 percent. It is projected that the output gap would continue shrinking and would be closed in 2017.

Comparing the MV filter results with other estimates

Traditional methods do not capture legacy effects properly. Figure 3 shows a comparison of output gap estimate for the MV filter, CBO, and HP filter. For 2014, the MV filter’s and CBO’s extimates are very close to each other. However, the paths of these two series before are markedly different, with the CBO projecting a noticeably larger output gap after the GFC than the MV filter. This is because the MV filter interprets some of the capacity that was built in the runup to the crisis as a temporary shock. CBO’s methodlogy, howver, initally treats most of the capacity built in the run up to the crisis as permanent, given its deterministic-trend approach. This changes over years as weak data on growth inform CBO’s projections that a sizable part of the capacity built before the ciris was not permanent, brining CBO’s estimate closer to MV filter’s estimate over time, in the absence of additional crisis. Figure 3 also reports the results of an HP filter with the actual growth data up to 2014. In the next section we show that HP filter’s estimates vary significantly as more data is available, making it very unrelibale to estimate the potential output and output gap.

Tackling the end-of-sample problem

A well known problem with filters, such as the HP, is the end-of-sample problem. As data is added to the end of the sample, projections change significantly. MV filters are also generally subject to this end-of-sample problem. The MV filter resolves this issue by drawing on information from growth and inflation expectations. Figures 3 shows estimates of output gap, using our MV filter, as well as the HP filter. Estimates of potentail output for the MV filter are presented in Figure 4, and for the HP filter are presented in Figure 5. In Figures 4-5, potential output is projected for 5 periods ahead and with each additional year, the sample is extended by one year and potential output is reestimated. Out-of-sample estimates from the MV filter remain much closer to the final (current) estimate, which is shown in black, while for the HP filter, out-of-sample estimates substantially deviate from the final (current) estimate. This is largely because the MV filter uses the information in growth and inflation expectations to inform its projections, but the HP filter does not.

Conclusion

So, have the scars from the GFC healed? Not yet. The output gap is still negative and potential has yet to recover to its new long-term level. But irrespective of what methodology is used, there is a consensus that the output gap is shrinking.

This post written by Ali Alichi.

1. In Figure 1, real GDP growth until 2014 is from actual data from the Bureau of Economic Analysis, but for 2015 and onwards, it is MV filtering model’s projection. Potential GDP growth is model’s estimate\projection for the entire sample.

My first observation is that it’s kind of surprising that import prices haven’t been declining for a longer period. Year-on-year Chinese PPI inflation has been negative since early 2012. The second observation is this means one has to be a little careful about inferring a contracting economy from price trends, given the PPI has been declining even during periods when the economy was widely viewed as growing).

Of course, one critical factor is omitted from the graph — namely the exchange rate. Recall the identity (in logs) that the import price equals the export price converted by the exchange rate:

p$Import = s$/¥ + p¥Export

Where p$Import is the log dollar price of imported goods from China, s$/¥ is the log exchange rate, in number of USD per CNY, and p¥Export is the log CNY price of Chinese exports to the US.

Taking the first difference:

Δp$Import = Δs$/¥ + Δ p¥Export

That means the growth rate of dollar import prices equals the depreciation rate of the dollar against the yuan plus the yuan price of exports.

Since this is an identity, it doesn’t speak to the actual correlations observed in reality, since the yuan price of exports is likely to respond to exchange rate changes (i.e., p¥Export is not exogenous). (In Figure 1, I’m using the PPI as a proxy for the exogenous component of export prices.)

These plots sidestep the issue of dynamics. What’s true is that the dollar has appreciated against the yuan by about 2.5% since 2014M01, while import prices fell by 0.8%. I suspect what happens to import prices will probably depend a lot more on the bilateral exchange rate than production costs in China. (Which is not to say the state of the economy generally won’t affect the government’s setting of the exchange rate.)

My back of the envelope estimates suggest that over the 2005-2015 period, exchange rate pass through is about 0.45, not far off from my 2011 estimate of 0.52 (pass through of PPI into import prices is only about 0.20). This is below estimates based on disaggregated sectoral data and the 2005 RMB regime reform (e.g., Auer, 2011) (see also Kim, Nam, Wang and Wu (2015) for a more recent period).

Note that the exchange rate pass through coefficient will vary depending on a variety of factors, including competition from domestic firms in the US, market share, and the imported component of Chinese exports (see e.g., CBO).

]]>http://econbrowser.com/archives/2015/07/a-brief-note-on-prices-of-imported-chinese-goods/feed0Guest Contribution: ‘Only Tsipras Can “Go to China”’http://econbrowser.com/archives/2015/07/guest-contribution-only-tsipras-can-go-to-china
http://econbrowser.com/archives/2015/07/guest-contribution-only-tsipras-can-go-to-china#commentsThu, 23 Jul 2015 05:53:57 +0000http://econbrowser.com/?p=24412Today we are fortunate to have a guest contribution written by Jeffrey Frankel, Harpel Professor of Capital Formation and Growth at Harvard University, and former Member of the Council of Economic Advisers, 1997-99. An earlier version was published in Project Syndicate.

Alexis Tsipras, the Greek prime minister, has the chance to play a role for his country analogous to the roles played by Korean President Kim Dae Jung in 1997 and Brazilian President Luiz Inácio Lula da Silva in 2002. Both of those presidential candidates had been long-time men of the left, with strong ties to labor, and were believed to place little priority on fiscal responsibility or free markets. Both were elected at a time of economic crisis in their respective countries. Both confronted financial and international constraints in office that had not been especially salient in their minds when they were opposition politicians. Both were able soon to make the mental and political adjustment to the realities faced by debtor economies. This flexibility helped both to lead their countries more effectively.

The two new presidents launched needed reforms. Some of these were “conservative” reforms (or “neo-liberal”) that might not have been possible under more mainstream or conservative politicians.

But Kim and Lula were also able to implement other reforms consistent with their lifetime commitment to reducing income inequality. South Korea under Kim began to rein in the chaebols, the country’s huge family-owned conglomerates. Brazil under Lula expanded Bolsa Familia, a system of direct cash payments to households that is credited with lifting millions out of poverty.

Mr. Tsipras and his Syriza party, by contrast, spent their first six months in office still mentally blinkered against financial and international realities. A career as a political party apparatchik is probably not the best training for being able to see things from the perspective of other points on the political spectrum, other segments of the economy, or other countries. This is true of a career in any political party in any country but especially one on the far left or far right.

The Greek Prime Minister seemed to think that calling the July 5 referendum on whether to accept terms that had been demanded previously by Germany and the other creditor countries would strengthen his bargaining position. If he were reading from a normal script, he would logically have been asking the Greek people to vote “yes” on the referendum. But he was asking them to vote “no”, of course, which they did in surprisingly large numbers. As a result – and contrary to his apparent expectations — the only people’s whose bargaining position was strengthened by this referendum were those Germans who felt the time had come to let Greece drop out of the euro.

The Greek leadership discovered that its euro partners, predictably, are not prepared to offer easier terms than they had been in June, and in fact are asking for more extensive concessions as the price of a third bailout. Only then, a week after the referendum, did Mr. Tsipras finally begin to face up to reality.

The only possible silver lining to this sorry history is that some of his supporters at home may – paradoxically – now be willing to swallow the bitter medicine that they had opposed in the referendum. One should not underestimate the opposition that reforms will continue to face among Greeks, in light of the economic hardship already suffered. But like Kim dae Jung and Lula, he may be able to bring political support of some on the left who figure, “If my leader now says these unpalatable measures are necessary, then it must be true”. As they say, Only Nixon can go to China.

None of this is to say that the financial and international realities are necessarily always reasonable. Sometimes global financial markets indulge in unreasonable booms in their eagerness to lend, followed by abrupt reversals. That describes the large capital inflows into Greece and other European periphery countries in the first ten years after the euro’s 1999 birth. It also describes the sudden stop in lending to Korea and other emerging market countries in the late 1990s.

Foreign creditor governments can be unreasonable as well. The misperceptions and errors on the part of leaders in Germany and other creditor countries have been as bad as the misperceptions and errors on the part of the less-experienced Greek leaders. For example the belief that fiscal austerity raises income rather than lowering it, even in the short run, was a mistaken perception. The refusal to write down the debt especially in 2010, when most of it was still in the hands of private creditors, was a mistaken policy. These mistakes explain why the Greek debt/GDP ratio is so much higher today than in 2010 — much higher than was forecast.

A stubborn clinging to wrong propositions on each side has reinforced the stubbornness on the other side. The Germans would have done better to understand and admit explicitly that fiscal austerity is contractionary in the short run. The Greeks would have done better to understand and admit explicitly that the preeminence of democracy does not mean that one country’s people can democratically vote for other countries to give them money.

In terms of game theory, the fact that the Greeks and Germans have different economic interests is not enough to explain the very poor outcome of negotiations to date. The difference in perceptions has been central. “Getting to yes” in a bargaining situation requires not just that the negotiators have a clear idea of their own top priorities, but also a good idea of what is the top priority of the other side. We may now be facing a “bad bargain” in which each side is called upon to give up its top priorities. On one side, Greece shouldn’t expect the ECB and the IMF to be willing explicitly to write down the debt they hold. On the other side, the creditors shouldn’t expect Greece to run a substantial primary budget surplus. A “good bargain” would have the creditors stretch out lending terms even further so that Greece doesn’t have to pay over the next few years and would have the Greeks committing to structural reforms that would raise growth.

One hopes that the awful experience of the recent past has led both sides to clearer perceptions of economic realities and of top priorities. Such evolution is necessary if the two sides are to arrive at a good bargain rather than either a bad bargain or a failure of cooperation altogether. The non-cooperative equilibrium is that Greek banks fail and Greece effectively drops out of the euro. This may be even worse than a bad bargain, although I am not sure.

Admittedly, both Kim and Lula had their flaws. Moreover, Korea and Brazil had some advantages that Greece lacks, beyond Syriza’s delay in adapting to realities. They had their own currencies. They were able to boost exports in the years following their currency crises.

But a recurrent theme of the Greek crisis ever since it erupted in late 2009 is that both the Greeks and the Euro creditor countries have been reluctant to realize that lessons from previous emerging market crises might apply to their situation. After all, they said, Greece was not a developing country but rather a member of the euro. (This is the reason, for example, why Frankfurt and Brussels at first did not want Greece to go to the IMF and did not want to write down the Greek debt.) But the emerging market crises do have useful lessons for Europe. If Tsipras were able to shift gears in the way that Kim dae Jung did in Korea and Lula did in Brazil, he would better serve his country.